What is Value at Risk?

Value at Risk (VaR) is a single-number estimate of the maximum loss a portfolio is expected to suffer over a set time horizon at a given confidence level, under normal market conditions. A 95%, one-day VaR of $2m means that on roughly 95% of days the loss should not exceed $2m — or, put the other way, on about 1 day in 20 you'd expect a loss larger than that. It always has two parameters: a horizon (how far ahead) and a confidence level (how sure). It's a way of compressing the market risk in an entire book of positions into one comparable figure.

How is VaR calculated?

There are three standard methods. Historical simulation re-prices your current positions using actual market moves from a past window and reads the loss at the chosen percentile. The variance-covariance (parametric) method assumes returns are normally distributed and derives VaR from the volatilities and correlations of your risk factors. Monte Carlo simulation generates thousands of random market scenarios from an assumed statistical model and re-prices the portfolio in each. All three aim at the same number; they differ in their assumptions and in how much computation and modelling they demand.

What are the limitations of VaR?

VaR tells you a threshold, not what lies beyond it — a 95% VaR says nothing about how bad the worst 5% of days actually get, which is exactly the tail that hurts. It's backward-looking: historical and parametric methods assume the future resembles a chosen past window, so it underestimates risk in calm markets and lags regime changes. It's highly sensitive to its assumptions — the horizon, confidence level, data window and distribution you pick all move the answer. For those reasons many corporates lean on cash-flow-based measures rather than treating a bank-style VaR as the whole story.

Value at Risk (VaR) in Corporate Treasury

Value at Risk (VaR) is a statistical estimate of the largest loss a portfolio is expected to suffer over a set horizon at a given confidence level — a way of squeezing the market risk in a whole book of positions into a single, comparable number. It answers a specific question: under normal conditions, how bad might a bad day get? Used well, it's a genuinely useful lens on interest rate and FX exposure. Used naively, it lulls people into thinking one number captures a risk it was never designed to describe.

(This is a plain explanation of a risk measure, not investment or hedging advice for any particular situation.)

What VaR actually is

VaR has two parameters, and you can't quote it without both: a horizon (how far ahead — one day, ten days, a month) and a confidence level (how sure — 95%, 99%). Fix those, and VaR is the loss threshold you don't expect to breach.

The canonical example: a 95%, one-day VaR of $2m means that, under normal market conditions, on roughly 95% of days the loss on that portfolio should not exceed $2m. Flip it around and it's easier to feel: on about 1 day in 20, you'd expect to lose more than $2m. That's the whole idea — one figure that says "this is roughly the edge of an ordinary bad day."

What makes it powerful is aggregation. A treasury book might hold a dozen currencies, several rate exposures and a handful of instruments. VaR rolls all of that — including how the pieces move together — into one number you can track over time and compare across desks. That single-number quality is exactly why boards like it, and also exactly why it's dangerous.

The three ways to calculate it

There are three standard methods. They chase the same number and differ mainly in what they assume and how much machinery they need.

Historical simulation. Take your current positions and re-price them using the actual market moves from a past window — say the last year or two of daily changes. Sort the resulting hypothetical gains and losses and read off the loss at your chosen percentile. It makes no assumption about the shape of the distribution; its weakness is that it assumes the future looks like the window you picked.
Variance-covariance (parametric). Assume the risk factors' returns are normally distributed, then derive VaR from their volatilities and the correlations between them. It's fast and light — but the normal-distribution assumption understates the fat tails real markets have, so it tends to be optimistic about extreme moves.
Monte Carlo simulation. Generate thousands of random market scenarios from an assumed statistical model, re-price the whole portfolio in each, and read the percentile off the simulated distribution. It's the most flexible — it copes with non-linear instruments the other two struggle with — and the most computationally expensive, and it's still only as good as the model you feed it.

None of these is "correct." Each trades a different assumption for a different convenience, and a number that changes depending on which method you chose is a number to treat with respect, not reverence.

What treasury uses it for

In a corporate treasury, VaR earns its keep in a few concrete places. It sizes market risk across an FX or interest-rate portfolio, turning "we're exposed to a lot of currencies" into a figure you can act on. It's a natural board and management reporting metric — one line that says how much market risk the company is carrying this quarter versus last. And it underpins risk limits: a policy can state that portfolio VaR must stay under an agreed ceiling, giving treasury an objective, monitorable boundary rather than a matter of judgement. That limit-setting role is where VaR fits into the broader treasury risk management framework — it's a measurement tool serving a governance job.

VaR tells you where the edge of an ordinary bad day sits. It tells you nothing about the cliff on the other side — and it's the cliff, not the edge, that sinks companies.

Where VaR falls short

This is the part the marketing brochures skip, and the part 18 years teaches you to say out loud.

It says nothing about the tail. A 95% VaR is a threshold; it is silent on how bad the worst 5% of outcomes get. Two portfolios can share the same VaR while one loses a little beyond it and the other loses catastrophically. The losses that actually threaten a business live in that tail — precisely where VaR stops describing anything.

It's backward-looking. Historical and parametric methods assume the future resembles a chosen slice of the past. In calm markets that makes VaR complacent; when a regime changes, it lags, and the number stays reassuring right up until it isn't. VaR is a rear-view mirror with a confidence interval.

It's assumption-sensitive. The horizon, the confidence level, the length of the data window, the assumed distribution — change any of them and the number moves, sometimes a lot. A figure that malleable demands you understand the choices behind it, not just the output.

Which is why corporates often lean on cash-flow-based measures instead of a bank-style VaR. A bank runs trading books marked to market daily; a market-value loss distribution is the right language for it. A corporate mostly cares whether tomorrow's cash flows survive a rate or currency move — an operating question, not a mark-to-market one. That's the pitch of Cash Flow at Risk, the corporate-friendly companion to VaR: same statistical idea, aimed at cash flows and earnings rather than portfolio value, which is usually what a treasurer is actually trying to protect.

How to hold it

Treat VaR as one instrument on the panel, not the whole dashboard. It's excellent for comparing risk over time, aggregating a messy book into one figure, and anchoring a limit. It's useless as a promise about disasters. Pair it with stress tests and scenario analysis for the tail it ignores, quote it always with its horizon and confidence level, and know which method produced it. Do that, and VaR becomes what it was meant to be — a disciplined summary of ordinary risk, sitting inside the wider treasury risk management framework rather than pretending to be all of it.

Part of the Treasury Risk Management guide. See also Cash Flow at Risk (CFaR) and interest rate risk in corporate treasury. The newsletter sends one finance-systems pattern every two weeks.